Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations678012
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.7 MiB
Average record size in memory100.0 B

Variable types

Numeric8
Categorical5

Alerts

Area is highly overall correlated with DensityHigh correlation
BonusMalus is highly overall correlated with DrivAgeHigh correlation
Density is highly overall correlated with AreaHigh correlation
DrivAge is highly overall correlated with BonusMalusHigh correlation
IDpol has unique values Unique
VehAge has 57739 (8.5%) zeros Zeros
ClaimCount has 643952 (95.0%) zeros Zeros

Reproduction

Analysis started2025-08-27 08:10:45.608374
Analysis finished2025-08-27 08:11:03.106006
Duration17.5 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

IDpol
Real number (ℝ)

Unique 

Distinct678012
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2621860.6
Minimum1
Maximum6114330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-08-27T09:11:03.295245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile69365.55
Q11157951.8
median2272152.5
Q34046274.2
95-th percentile6014195.3
Maximum6114330
Range6114329
Interquartile range (IQR)2888322.5

Descriptive statistics

Standard deviation1641781.1
Coefficient of variation (CV)0.62618931
Kurtosis-0.65834502
Mean2621860.6
Median Absolute Deviation (MAD)1152062
Skewness0.23788976
Sum1.777653 × 1012
Variance2.6954452 × 1012
MonotonicityNot monotonic
2025-08-27T09:11:03.363253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72150 1
 
< 0.1%
2124053 1
 
< 0.1%
1049168 1
 
< 0.1%
134313 1
 
< 0.1%
1145209 1
 
< 0.1%
2281532 1
 
< 0.1%
4122208 1
 
< 0.1%
4128877 1
 
< 0.1%
2102858 1
 
< 0.1%
1106637 1
 
< 0.1%
Other values (678002) 678002
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
21 1
< 0.1%
ValueCountFrequency (%)
6114330 1
< 0.1%
6114329 1
< 0.1%
6114328 1
< 0.1%
6114327 1
< 0.1%
6114326 1
< 0.1%
6114325 1
< 0.1%
6114324 1
< 0.1%
6114323 1
< 0.1%
6114322 1
< 0.1%
6114321 1
< 0.1%

VehPower
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4546306
Minimum4
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-08-27T09:11:03.417253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q15
median6
Q37
95-th percentile11
Maximum15
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0509071
Coefficient of variation (CV)0.31774198
Kurtosis1.6682028
Mean6.4546306
Median Absolute Deviation (MAD)1
Skewness1.1713449
Sum4376317
Variance4.2062199
MonotonicityNot monotonic
2025-08-27T09:11:03.463263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 148976
22.0%
7 145400
21.4%
5 124821
18.4%
4 115349
17.0%
8 46956
 
6.9%
10 31354
 
4.6%
9 30085
 
4.4%
11 18352
 
2.7%
12 8214
 
1.2%
13 3229
 
0.5%
Other values (2) 5276
 
0.8%
ValueCountFrequency (%)
4 115349
17.0%
5 124821
18.4%
6 148976
22.0%
7 145400
21.4%
8 46956
 
6.9%
9 30085
 
4.4%
10 31354
 
4.6%
11 18352
 
2.7%
12 8214
 
1.2%
13 3229
 
0.5%
ValueCountFrequency (%)
15 2926
 
0.4%
14 2350
 
0.3%
13 3229
 
0.5%
12 8214
 
1.2%
11 18352
 
2.7%
10 31354
 
4.6%
9 30085
 
4.4%
8 46956
 
6.9%
7 145400
21.4%
6 148976
22.0%

VehAge
Real number (ℝ)

Zeros 

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0442603
Minimum0
Maximum100
Zeros57739
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-08-27T09:11:03.524782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q311
95-th percentile17
Maximum100
Range100
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.6662346
Coefficient of variation (CV)0.8043761
Kurtosis6.522051
Mean7.0442603
Median Absolute Deviation (MAD)4
Skewness1.1479909
Sum4776093
Variance32.106215
MonotonicityNot monotonic
2025-08-27T09:11:03.593097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 71284
 
10.5%
2 59124
 
8.7%
0 57739
 
8.5%
3 50261
 
7.4%
4 43492
 
6.4%
5 38737
 
5.7%
10 38394
 
5.7%
6 35717
 
5.3%
7 32880
 
4.8%
8 32680
 
4.8%
Other values (68) 217704
32.1%
ValueCountFrequency (%)
0 57739
8.5%
1 71284
10.5%
2 59124
8.7%
3 50261
7.4%
4 43492
6.4%
5 38737
5.7%
6 35717
5.3%
7 32880
4.8%
8 32680
4.8%
9 31922
4.7%
ValueCountFrequency (%)
100 25
< 0.1%
99 23
< 0.1%
85 1
 
< 0.1%
84 1
 
< 0.1%
83 2
 
< 0.1%
82 1
 
< 0.1%
81 3
 
< 0.1%
80 3
 
< 0.1%
79 1
 
< 0.1%
78 1
 
< 0.1%

DrivAge
Real number (ℝ)

High correlation 

Distinct83
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.499153
Minimum18
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-08-27T09:11:03.660661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q134
median44
Q355
95-th percentile72
Maximum100
Range82
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.13743
Coefficient of variation (CV)0.31071854
Kurtosis-0.34268603
Mean45.499153
Median Absolute Deviation (MAD)10
Skewness0.43575894
Sum30848972
Variance199.86694
MonotonicityNot monotonic
2025-08-27T09:11:03.727658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 17530
 
2.6%
38 17346
 
2.6%
39 17320
 
2.6%
37 17295
 
2.6%
52 17195
 
2.5%
34 17059
 
2.5%
40 17017
 
2.5%
51 17016
 
2.5%
41 16977
 
2.5%
42 16953
 
2.5%
Other values (73) 506304
74.7%
ValueCountFrequency (%)
18 748
 
0.1%
19 2392
 
0.4%
20 3676
 
0.5%
21 4437
 
0.7%
22 5291
0.8%
23 6261
0.9%
24 7392
1.1%
25 8697
1.3%
26 10301
1.5%
27 11827
1.7%
ValueCountFrequency (%)
100 3
 
< 0.1%
99 70
< 0.1%
98 5
 
< 0.1%
97 10
 
< 0.1%
96 15
 
< 0.1%
95 24
 
< 0.1%
94 32
 
< 0.1%
93 55
< 0.1%
92 66
< 0.1%
91 121
< 0.1%

BonusMalus
Real number (ℝ)

High correlation 

Distinct115
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.761464
Minimum50
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-08-27T09:11:03.794758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile50
Q150
median50
Q364
95-th percentile95
Maximum230
Range180
Interquartile range (IQR)14

Descriptive statistics

Standard deviation15.636639
Coefficient of variation (CV)0.26165087
Kurtosis2.6748529
Mean59.761464
Median Absolute Deviation (MAD)0
Skewness1.7289437
Sum40518990
Variance244.50448
MonotonicityNot monotonic
2025-08-27T09:11:04.006961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 384156
56.7%
100 19530
 
2.9%
68 18791
 
2.8%
72 18580
 
2.7%
76 18226
 
2.7%
64 18192
 
2.7%
80 18086
 
2.7%
57 17938
 
2.6%
60 17363
 
2.6%
54 17360
 
2.6%
Other values (105) 129790
 
19.1%
ValueCountFrequency (%)
50 384156
56.7%
51 15869
 
2.3%
52 4770
 
0.7%
53 3351
 
0.5%
54 17360
 
2.6%
55 5593
 
0.8%
56 3453
 
0.5%
57 17938
 
2.6%
58 5970
 
0.9%
59 2779
 
0.4%
ValueCountFrequency (%)
230 1
 
< 0.1%
228 1
 
< 0.1%
218 1
 
< 0.1%
208 1
 
< 0.1%
198 2
 
< 0.1%
196 3
< 0.1%
195 6
< 0.1%
190 3
< 0.1%
187 3
< 0.1%
185 5
< 0.1%

VehBrand
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
B12
166024 
B1
162736 
B2
159861 
B3
53394 
B5
34753 
Other values (6)
101244 

Length

Max length3
Median length2
Mean length2.3149517
Min length2

Characters and Unicode

Total characters1569565
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowB12
3rd rowB1
4th rowB12
5th rowB1

Common Values

ValueCountFrequency (%)
B12 166024
24.5%
B1 162736
24.0%
B2 159861
23.6%
B3 53394
 
7.9%
B5 34753
 
5.1%
B6 28548
 
4.2%
B4 25179
 
3.7%
B10 17707
 
2.6%
B11 13585
 
2.0%
B13 12178
 
1.8%

Length

2025-08-27T09:11:04.066444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b12 166024
24.5%
b1 162736
24.0%
b2 159861
23.6%
b3 53394
 
7.9%
b5 34753
 
5.1%
b6 28548
 
4.2%
b4 25179
 
3.7%
b10 17707
 
2.6%
b11 13585
 
2.0%
b13 12178
 
1.8%

Most occurring characters

ValueCountFrequency (%)
B 678012
43.2%
1 389862
24.8%
2 325885
20.8%
3 65572
 
4.2%
5 34753
 
2.2%
4 29226
 
1.9%
6 28548
 
1.8%
0 17707
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1569565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 678012
43.2%
1 389862
24.8%
2 325885
20.8%
3 65572
 
4.2%
5 34753
 
2.2%
4 29226
 
1.9%
6 28548
 
1.8%
0 17707
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1569565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 678012
43.2%
1 389862
24.8%
2 325885
20.8%
3 65572
 
4.2%
5 34753
 
2.2%
4 29226
 
1.9%
6 28548
 
1.8%
0 17707
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1569565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 678012
43.2%
1 389862
24.8%
2 325885
20.8%
3 65572
 
4.2%
5 34753
 
2.2%
4 29226
 
1.9%
6 28548
 
1.8%
0 17707
 
1.1%

VehGas
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
Regular
345876 
Diesel
332136 

Length

Max length7
Median length7
Mean length6.5101326
Min length6

Characters and Unicode

Total characters4413948
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowRegular
3rd rowRegular
4th rowRegular
5th rowDiesel

Common Values

ValueCountFrequency (%)
Regular 345876
51.0%
Diesel 332136
49.0%

Length

2025-08-27T09:11:04.118851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-27T09:11:04.158419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
regular 345876
51.0%
diesel 332136
49.0%

Most occurring characters

ValueCountFrequency (%)
e 1010148
22.9%
l 678012
15.4%
R 345876
 
7.8%
g 345876
 
7.8%
u 345876
 
7.8%
a 345876
 
7.8%
r 345876
 
7.8%
D 332136
 
7.5%
i 332136
 
7.5%
s 332136
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4413948
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1010148
22.9%
l 678012
15.4%
R 345876
 
7.8%
g 345876
 
7.8%
u 345876
 
7.8%
a 345876
 
7.8%
r 345876
 
7.8%
D 332136
 
7.5%
i 332136
 
7.5%
s 332136
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4413948
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1010148
22.9%
l 678012
15.4%
R 345876
 
7.8%
g 345876
 
7.8%
u 345876
 
7.8%
a 345876
 
7.8%
r 345876
 
7.8%
D 332136
 
7.5%
i 332136
 
7.5%
s 332136
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4413948
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1010148
22.9%
l 678012
15.4%
R 345876
 
7.8%
g 345876
 
7.8%
u 345876
 
7.8%
a 345876
 
7.8%
r 345876
 
7.8%
D 332136
 
7.5%
i 332136
 
7.5%
s 332136
 
7.5%

Area
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
C
191880 
D
151595 
E
137167 
A
103957 
B
75459 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters678012
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowD
3rd rowE
4th rowC
5th rowE

Common Values

ValueCountFrequency (%)
C 191880
28.3%
D 151595
22.4%
E 137167
20.2%
A 103957
15.3%
B 75459
 
11.1%
F 17954
 
2.6%

Length

2025-08-27T09:11:04.201445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-27T09:11:04.245491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
c 191880
28.3%
d 151595
22.4%
e 137167
20.2%
a 103957
15.3%
b 75459
 
11.1%
f 17954
 
2.6%

Most occurring characters

ValueCountFrequency (%)
C 191880
28.3%
D 151595
22.4%
E 137167
20.2%
A 103957
15.3%
B 75459
 
11.1%
F 17954
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 678012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 191880
28.3%
D 151595
22.4%
E 137167
20.2%
A 103957
15.3%
B 75459
 
11.1%
F 17954
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 678012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 191880
28.3%
D 151595
22.4%
E 137167
20.2%
A 103957
15.3%
B 75459
 
11.1%
F 17954
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 678012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 191880
28.3%
D 151595
22.4%
E 137167
20.2%
A 103957
15.3%
B 75459
 
11.1%
F 17954
 
2.6%

Density
Real number (ℝ)

High correlation 

Distinct1607
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1792.4223
Minimum1
Maximum27000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-08-27T09:11:04.310596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q192
median393
Q31658
95-th percentile7313
Maximum27000
Range26999
Interquartile range (IQR)1566

Descriptive statistics

Standard deviation3958.6495
Coefficient of variation (CV)2.2085473
Kurtosis24.86941
Mean1792.4223
Median Absolute Deviation (MAD)355
Skewness4.6514178
Sum1.2152838 × 109
Variance15670906
MonotonicityNot monotonic
2025-08-27T09:11:04.378999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27000 10515
 
1.6%
3317 9891
 
1.5%
1313 7157
 
1.1%
9307 5986
 
0.9%
3744 5540
 
0.8%
1326 5447
 
0.8%
405 5195
 
0.8%
4128 5055
 
0.7%
4762 4985
 
0.7%
57 4262
 
0.6%
Other values (1597) 613979
90.6%
ValueCountFrequency (%)
1 7
 
< 0.1%
2 92
 
< 0.1%
3 304
 
< 0.1%
4 274
 
< 0.1%
5 438
 
0.1%
6 752
 
0.1%
7 1088
 
0.2%
8 1131
 
0.2%
9 1813
0.3%
10 2911
0.4%
ValueCountFrequency (%)
27000 10515
1.6%
23396 66
 
< 0.1%
22821 182
 
< 0.1%
22669 463
 
0.1%
21410 76
 
< 0.1%
20000 6
 
< 0.1%
18229 200
 
< 0.1%
17140 910
 
0.1%
16533 613
 
0.1%
16291 175
 
< 0.1%

Region
Categorical

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
Centre
160601 
Rhone-Alpes
84751 
Provence-Alpes-Cotes-D'Azur
79315 
Ile-de-France
69791 
Bretagne
42122 
Other values (16)
241432 

Length

Max length27
Median length17
Mean length12.962977
Min length5

Characters and Unicode

Total characters8789054
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentre
2nd rowPays-de-la-Loire
3rd rowProvence-Alpes-Cotes-D'Azur
4th rowPays-de-la-Loire
5th rowProvence-Alpes-Cotes-D'Azur

Common Values

ValueCountFrequency (%)
Centre 160601
23.7%
Rhone-Alpes 84751
12.5%
Provence-Alpes-Cotes-D'Azur 79315
11.7%
Ile-de-France 69791
10.3%
Bretagne 42122
 
6.2%
Nord-Pas-de-Calais 40275
 
5.9%
Pays-de-la-Loire 38751
 
5.7%
Languedoc-Roussillon 35805
 
5.3%
Aquitaine 31329
 
4.6%
Poitou-Charentes 19046
 
2.8%
Other values (11) 76226
11.2%

Length

2025-08-27T09:11:04.444462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
centre 160601
23.7%
rhone-alpes 84751
12.5%
provence-alpes-cotes-d'azur 79315
11.7%
ile-de-france 69791
10.3%
bretagne 42122
 
6.2%
nord-pas-de-calais 40275
 
5.9%
pays-de-la-loire 38751
 
5.7%
languedoc-roussillon 35805
 
5.3%
aquitaine 31329
 
4.6%
poitou-charentes 19046
 
2.8%
Other values (11) 76226
11.2%

Most occurring characters

ValueCountFrequency (%)
e 1462867
16.6%
- 795377
 
9.0%
n 626133
 
7.1%
r 598675
 
6.8%
o 518984
 
5.9%
s 503548
 
5.7%
a 453346
 
5.2%
l 386693
 
4.4%
t 361569
 
4.1%
C 308105
 
3.5%
Other values (24) 2773757
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8789054
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1462867
16.6%
- 795377
 
9.0%
n 626133
 
7.1%
r 598675
 
6.8%
o 518984
 
5.9%
s 503548
 
5.7%
a 453346
 
5.2%
l 386693
 
4.4%
t 361569
 
4.1%
C 308105
 
3.5%
Other values (24) 2773757
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8789054
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1462867
16.6%
- 795377
 
9.0%
n 626133
 
7.1%
r 598675
 
6.8%
o 518984
 
5.9%
s 503548
 
5.7%
a 453346
 
5.2%
l 386693
 
4.4%
t 361569
 
4.1%
C 308105
 
3.5%
Other values (24) 2773757
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8789054
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1462867
16.6%
- 795377
 
9.0%
n 626133
 
7.1%
r 598675
 
6.8%
o 518984
 
5.9%
s 503548
 
5.7%
a 453346
 
5.2%
l 386693
 
4.4%
t 361569
 
4.1%
C 308105
 
3.5%
Other values (24) 2773757
31.6%

Group
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
1
136195 
5
135569 
4
135554 
2
135378 
3
135316 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters678012
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 136195
20.1%
5 135569
20.0%
4 135554
20.0%
2 135378
20.0%
3 135316
20.0%

Length

2025-08-27T09:11:04.500527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-27T09:11:04.543092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 136195
20.1%
5 135569
20.0%
4 135554
20.0%
2 135378
20.0%
3 135316
20.0%

Most occurring characters

ValueCountFrequency (%)
1 136195
20.1%
5 135569
20.0%
4 135554
20.0%
2 135378
20.0%
3 135316
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 678012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 136195
20.1%
5 135569
20.0%
4 135554
20.0%
2 135378
20.0%
3 135316
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 678012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 136195
20.1%
5 135569
20.0%
4 135554
20.0%
2 135378
20.0%
3 135316
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 678012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 136195
20.1%
5 135569
20.0%
4 135554
20.0%
2 135378
20.0%
3 135316
20.0%

Exposure
Real number (ℝ)

Distinct181
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52874953
Minimum0.00273224
Maximum2.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 MiB
2025-08-27T09:11:04.604275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.00273224
5-th percentile0.04
Q10.18
median0.49
Q30.99
95-th percentile1
Maximum2.01
Range2.0072678
Interquartile range (IQR)0.81

Descriptive statistics

Standard deviation0.36444151
Coefficient of variation (CV)0.68925169
Kurtosis-1.5242423
Mean0.52874953
Median Absolute Deviation (MAD)0.37
Skewness0.08532088
Sum358498.53
Variance0.13281761
MonotonicityNot monotonic
2025-08-27T09:11:04.677310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 168125
24.8%
0.08 44670
 
6.6%
0.07 12969
 
1.9%
0.24 12950
 
1.9%
0.5 12497
 
1.8%
0.49 12298
 
1.8%
0.03 11996
 
1.8%
0.04 11131
 
1.6%
0.12 11047
 
1.6%
0.2 8727
 
1.3%
Other values (171) 371602
54.8%
ValueCountFrequency (%)
0.00273224 1060
 
0.2%
0.002739726 2045
 
0.3%
0.005464481 609
 
0.1%
0.005479452 1396
 
0.2%
0.008196721 620
 
0.1%
0.008219178 1147
 
0.2%
0.01 6726
1.0%
0.02 5656
0.8%
0.03 11996
1.8%
0.04 11131
1.6%
ValueCountFrequency (%)
2.01 2
< 0.1%
2 1
< 0.1%
1.99 1
< 0.1%
1.98 1
< 0.1%
1.93 1
< 0.1%
1.92 1
< 0.1%
1.9 2
< 0.1%
1.88 1
< 0.1%
1.85 2
< 0.1%
1.82 1
< 0.1%

ClaimCount
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.052447449
Minimum0
Maximum16
Zeros643952
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-08-27T09:11:04.734384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.23480476
Coefficient of variation (CV)4.476953
Kurtosis73.267147
Mean0.052447449
Median Absolute Deviation (MAD)0
Skewness5.4175084
Sum35560
Variance0.055133278
MonotonicityNot monotonic
2025-08-27T09:11:04.781409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 643952
95.0%
1 32687
 
4.8%
2 1298
 
0.2%
3 62
 
< 0.1%
4 5
 
< 0.1%
11 2
 
< 0.1%
5 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
0 643952
95.0%
1 32687
 
4.8%
2 1298
 
0.2%
3 62
 
< 0.1%
4 5
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
11 2
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 5
 
< 0.1%
3 62
 
< 0.1%
2 1298
 
0.2%
1 32687
4.8%

Interactions

2025-08-27T09:11:01.059581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:54.689183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:55.592992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:56.472008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:57.354009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:58.224741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:59.125339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:00.141874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:01.169581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:54.803180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:55.700992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:56.580977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:57.464600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:58.336741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:59.233385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:00.256808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:01.276580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:54.914181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:55.808991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:56.686975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:57.572600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:58.449740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:59.473884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:00.369780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:01.384581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:55.029181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:55.917004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:56.792976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:57.677547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:58.559753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:59.581886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:00.483661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:01.491582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:55.139181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:56.025997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:56.899987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:57.780601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:58.667755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:59.688866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:00.595542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:01.605581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:55.251180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:56.137008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:57.019985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:57.891612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:58.778755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:59.804867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:00.713542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:01.713581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:55.363180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:56.247007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:57.129000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:58.003644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:58.889754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:59.910865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:00.827556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:01.826581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:55.478189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:56.359008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:57.241998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:58.114694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:10:59.006825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:00.025874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-27T09:11:00.942567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-27T09:11:04.830419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AreaBonusMalusClaimCountDensityDrivAgeExposureGroupIDpolRegionVehAgeVehBrandVehGasVehPower
Area1.0000.0510.0060.5890.0290.0580.0000.0560.3180.0400.0730.1310.038
BonusMalus0.0511.0000.0380.139-0.571-0.1960.000-0.0110.0290.0820.0210.048-0.068
ClaimCount0.0060.0381.0000.0130.0120.0710.000-0.1410.005-0.0220.0000.002-0.003
Density0.5890.1390.0131.000-0.044-0.1230.0010.0620.235-0.1020.0490.102-0.012
DrivAge0.029-0.5710.012-0.0441.0000.1640.0000.0580.041-0.0780.0510.1190.040
Exposure0.058-0.1960.071-0.1230.1641.0000.000-0.1570.0910.1840.0910.040-0.036
Group0.0000.0000.0000.0010.0000.0001.0000.0000.0010.0020.0000.0000.000
IDpol0.056-0.011-0.1410.0620.058-0.1570.0001.0000.138-0.1200.2300.0500.032
Region0.3180.0290.0050.2350.0410.0910.0010.1381.0000.0660.1300.0870.045
VehAge0.0400.082-0.022-0.102-0.0780.1840.002-0.1200.0661.0000.1100.127-0.002
VehBrand0.0730.0210.0000.0490.0510.0910.0000.2300.1300.1101.0000.1160.154
VehGas0.1310.0480.0020.1020.1190.0400.0000.0500.0870.1270.1161.0000.280
VehPower0.038-0.068-0.003-0.0120.040-0.0360.0000.0320.045-0.0020.1540.2801.000

Missing values

2025-08-27T09:11:02.005581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-27T09:11:02.362874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDpolVehPowerVehAgeDrivAgeBonusMalusVehBrandVehGasAreaDensityRegionGroupExposureClaimCount
021240535.01.031.060.0B2DieselC393.0Centre20.530
110491684.02.073.050.0B12RegularD983.0Pays-de-la-Loire30.100
21343134.011.060.062.0B1RegularE3744.0Provence-Alpes-Cotes-D'Azur11.000
311452097.09.037.050.0B12RegularC204.0Pays-de-la-Loire10.060
422815325.04.043.054.0B1DieselE3317.0Provence-Alpes-Cotes-D'Azur30.500
541222087.015.074.050.0B1RegularA45.0Centre11.000
641288774.08.028.076.0B2RegularE3688.0Rhone-Alpes40.500
721028587.014.020.0100.0B1RegularD1329.0Ile-de-France50.140
811066376.00.053.058.0B1DieselC433.0Provence-Alpes-Cotes-D'Azur40.780
931663075.017.040.083.0B2RegularA10.0Auvergne20.430
IDpolVehPowerVehAgeDrivAgeBonusMalusVehBrandVehGasAreaDensityRegionGroupExposureClaimCount
67800241200496.06.070.064.0B2DieselC229.0Bretagne31.000
67800321073187.02.024.095.0B13RegularC226.0Centre40.040
67800440205838.08.027.085.0B12RegularF17140.0Ile-de-France20.120
67800510346435.011.058.080.0B2RegularC280.0Centre51.000
67800650368627.05.050.050.0B2DieselC267.0Ile-de-France30.720
67800741345066.04.061.050.0B2DieselC220.0Rhone-Alpes41.000
67800810379838.011.036.072.0B10DieselC282.0Centre30.040
67800931973897.011.050.050.0B2DieselA9.0Centre41.000
678010259347.020.034.052.0B1DieselC176.0Provence-Alpes-Cotes-D'Azur51.000
678011721507.013.033.050.0B2RegularC115.0Pays-de-la-Loire41.000